Manager, Data Science

Natera•San Carlos, CA

About The Position

Natera is a global leader in cell-free DNA testing, advancing diagnostics in oncology, women’s health, and organ health. The Data Science Manager in R&D Operations leads a team of data scientists in designing and implementing data solutions to accelerate R&D. The role owns operational data workflows, establishes scalable processes and SOPs, and applies machine learning and/or artificial intelligence to support informed decision-making and efficiency. The manager works closely with the Data Engineering Manager and Data Analytics Manager to deliver integrated, reliable data solutions. The position sets the data science technical direction and roadmap for R&D Operations, builds team capabilities, and reports progress to operational and executive stakeholders. It translates opportunities identified through analytics into process changes, algorithms, or tools, with a focus on measurable improvement in turnaround time and cost per activity. The role also owns sample governance, develops operational models and data products, establishes data governance and reproducibility practices, and partners across R&D and supporting functions to move solutions from prototype to production.

Requirements

At least 7 years of experience in data science in biotechnology, life sciences, or operations research.
Demonstrated people-management experience, including hiring, mentoring, and developing data scientists through coaching on performance and career growth.
Experience owning a data science roadmap and communicating progress to senior and executive stakeholders.
Working knowledge of a relevant scientific or operational domain—biology or life sciences strongly preferred, with operations research a close second—sufficient to partner effectively with R&D Operations stakeholders.
Experience with laboratory or operational data, such as LIMS, sample management, inventory, purchasing or procurement, or equipment operations.
Track record of driving process improvement by identifying root causes and delivering solutions through process changes, algorithms, or tools.
Strong background in building data processes, SOPs, and governance frameworks.
Expertise in Python, including pandas, scikit-learn, and TensorFlow or PyTorch, and SQL.
Hands-on expertise in machine learning and/or AI applied to real-world problems, including setting technical direction for predictive modeling and optimization solutions.
Experience taking machine learning models or data products from prototype to production, including monitoring and maintenance.
Experience with modern cloud data warehouse and transformation tooling, such as Snowflake and dbt.
Experience partnering with peer functions such as engineering and analytics to deliver end-to-end solutions, including writing requirements or technical specifications.
Clear, direct communication skills to translate complex data insights into actionable business recommendations.
Strong critical thinking and a drive for learning and problem-solving.

Nice To Haves

Advanced degree in biology, molecular biology, biochemistry, bioinformatics, or operations research.
Familiarity with data integrity and audit-readiness practices in a regulated GxP/GCLP environment.

Responsibilities

Direct, mentor, and develop a team of data scientists, including hiring, performance management, and career development.
Set the technical direction for the data science team and advance its machine learning and artificial intelligence capabilities.
Own the data science roadmap for R&D Operations, define success metrics, and report progress and status to R&D Operations and executive stakeholders.
Translate analytics-identified pain points and opportunities into process changes, algorithms, or tools, and drive implementation to achieve measurable process improvement, including efficiency gains in turnaround time and cost per activity.
Own the sample governance process end-to-end, ensuring accuracy, timeliness, and accountability, and manage related stakeholder relationships.
Develop and oversee machine learning models, optimization strategies, and AI solutions to forecast demand, optimize resource allocation, and automate operational processes.
Lead the development of new data products and related training and enablement to increase visibility, self-service, and data-driven decision-making across R&D Operations.
Design, document, and implement processes for data governance, data integrity, and reproducibility across R&D Operations.
Partner with the Data Engineering and Data Analytics Managers and collaborate across R&D and supporting functions to translate business needs into governed, reliable data solutions.
Write clear, testable requirements, such as acceptance criteria and data contracts, and define the path from prototype to production in partnership with Data Engineering.